The K = 2 conundrum.
Jasmine K. Janes,Jasmine K. Janes,Joshua M. Miller,Julian R. Dupuis,René M. Malenfant,Jamieson C. Gorrell,Jamieson C. Gorrell,Catherine I. Cullingham,Rose L. Andrew +8 more
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This review suggests that many studies may have been over‐ or underestimating population genetic structure; both scenarios have serious consequences, particularly with respect to conservation and management.Abstract:
Assessments of population genetic structure have become an increasing focus as they can provide valuable insight into patterns of migration and gene flow structure, the most highly cited of several clustering-based methods, was developed to provide robust estimates without the need for populations to be determined a priori structure introduces the problem of selecting the optimal number of clusters, and as a result, the ΔK method was proposed to assist in the identification of the "true" number of clusters In our review of 1,264 studies using structure to explore population subdivision, studies that used ΔK were more likely to identify K = 2 (54%, 443/822) than studies that did not use ΔK (21%, 82/386) A troubling finding was that very few studies performed the hierarchical analysis recommended by the authors of both ΔK and structure to fully explore population subdivision Furthermore, extensions of earlier simulations indicate that, with a representative number of markers, ΔK frequently identifies K = 2 as the top level of hierarchical structure, even when more subpopulations are present This review suggests that many studies may have been over- or underestimating population genetic structure; both scenarios have serious consequences, particularly with respect to conservation and management We recommend publication standards for population structure results so that readers can assess the implications of the results given their own understanding of the species biologyread more
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Demographic consequences of dispersal-related trait shift in two recently diverged taxa of montane grasshoppers.
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References
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Inference of population structure using multilocus genotype data
TL;DR: Pritch et al. as discussed by the authors proposed a model-based clustering method for using multilocus genotype data to infer population structure and assign individuals to populations, which can be applied to most of the commonly used genetic markers, provided that they are not closely linked.
Journal ArticleDOI
Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study.
TL;DR: It is found that in most cases the estimated ‘log probability of data’ does not provide a correct estimation of the number of clusters, K, and using an ad hoc statistic ΔK based on the rate of change in the log probability between successive K values, structure accurately detects the uppermost hierarchical level of structure for the scenarios the authors tested.
Journal ArticleDOI
Estimating F-statistics for the analysis of population structure.
Bruce S. Weir,C. Clark Cockerham +1 more
TL;DR: The purpose of this discussion is to offer some unity to various estimation formulae and to point out that correlations of genes in structured populations, with which F-statistics are concerned, are expressed very conveniently with a set of parameters treated by Cockerham (1 969, 1973).
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STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method
Dent Earl,Bridgett M. vonHoldt +1 more
TL;DR: STRUCTURE HARVESTER is presented, a web-based program for collating results generated by the program STRUCTURE, which provides a fast way to assess and visualize likelihood values across multiple values of K and hundreds of iterations for easier detection of the number of genetic groups that best fit the data.
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